In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees of freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna boresight directions can be independently adjusted to proactively improve wireless channel conditions for latency-critical users. We aim to minimize the maximum computation latency by jointly optimizing the MEC server computing resource allocation, receive beamforming, and the deflection angles of all RAs. To address the resulting non-convex problem, we develop an efficient alternating optimization (AO) framework. Specifically, the optimal edge computing resource allocation is derived based on the Karush-Kuhn-Tucker (KKT) conditions. Given the computing resources, the receive beamforming is optimized using semidefinite relaxation (SDR) combined with a bisection search. Furthermore, the RA deflection angles are optimized via fractional programming (FP) and successive convex approximation (SCA). Simulation results verify that the proposed RA-enabled MEC scheme significantly reduces the maximum computation latency compared with conventional benchmark methods.

翻译：在移动边缘计算（MEC）不断演进的背景下，增强通信可靠性与计算效率以支持日益严苛的低延迟服务仍是根本性挑战。可旋转天线（RA）作为一种有前景的技术，通过引入新的空间自由度（DoFs）来应对这一挑战。本文研究一种RA赋能的MEC系统，其中天线波束主瓣方向可独立调整，以主动改善延迟敏感用户的无线信道条件。我们通过联合优化MEC服务器计算资源分配、接收波束赋形及所有RAs的偏转角，旨在最小化最大计算延迟。针对由此产生的非凸问题，我们开发了一种高效的交替优化（AO）框架。具体而言，基于卡罗需-库恩-塔克（KKT）条件导出了最优边缘计算资源分配。在给定计算资源条件下，利用半定松弛（SDR）结合二分搜索优化接收波束赋形。此外，通过分数规划（FP）和逐次凸近似（SCA）优化了RA偏转角。仿真结果验证，与常规基准方法相比，所提出的RA赋能MEC方案能显著降低最大计算延迟。